This repository provides scripts and notebooks for the book "Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications." It serves as a practical resource for developers and researchers looking to learn and implement deep learning models using PyTorch, covering image, audio, and text data.

How It Works

The project offers a collection of Jupyter notebooks and Python scripts that demonstrate various deep learning concepts and their implementation in PyTorch. It follows a structured approach, aligning with the book's chapters, to guide users through building and deploying models. The inclusion of specific chapters on self-supervised learning and GPT-2 integration highlights a focus on contemporary deep learning techniques.

Quick Start & Requirements

• Installation:
  • Using Conda: conda env create --file environment.yml followed by conda activate myenv.
  • Using Pip: python3 -m venv myenv, source myenv/bin/activate (or Windows equivalent), then pip3 install -r requirements.txt.
• Prerequisites: Python, Conda or venv, pip, Jupyter Notebook/Lab.
• Optional: Google Colaboratory is recommended for GPU acceleration.
• Resources: A zip file containing the image dataset for Chapter 2 is available via a Google Drive link.
• Documentation: Installation guides for Python, Anaconda, pip, PyTorch, and Jupyter are linked.

Highlighted Details

• Includes code for self-supervised learning (Chapter 9.75).
• Features text generation with GPT-2 (Chapter 9.5).
• Covers model quantization techniques (Chapter 7.5).
• Provides a German translation of the book.

Maintenance & Community

The repository appears to have had updates in May 2020. No specific community channels or active contributor information is readily available in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

Some dataset download links mentioned in the README may be outdated, though an alternative zip file is provided. The repository's activity and community support are not clearly indicated.